Subtidal nearshore seagrass beds are important components of coastal ecotones. Understanding their association with shoreline morphology and shoreline stabilization interventions is critical to achieving a holistic approach to restoration. We combined geospatial analyses with field-based monitoring to better understand intra-ecotone associations between shorelines and nearshore seagrass beds in a shallow subtropical estuary. Using GIS analyses of aerial imagery, shoreline characterization models, and seagrass coverage models, the relationship between shoreline morphology, erosion, and yearly persistence of seagrass was examined in Mosquito Lagoon, FL between 2011 and 2021. Seagrass beds growing adjacent to stabilized “living shoreline” segments were monitored in the field throughout the 2023 growing season (March–November). GIS analyses indicate that within this microtidal system, seagrass persistence was associated with wider intertidal zones, shallower nearshore depths, and reduced rates of shoreline retreat. These shoreline morphological differences were all relatively small (< 1 m). Increased seagrass density along natural uneroded shorelines relative to three living shoreline designs was observed during field monitoring. Bayesian hierarchical models suggest that in this study, differences in seagrass density appeared attributable to shoreline slope rather than the stabilization treatment itself. Although the individual analyses in this study have relatively high levels of uncertainty, all suggest that seagrass suitability was greater along gradually sloped, slowly eroding shorelines. We recommend measurements of subtidal slope and adjacent SAV be incorporated into living shoreline monitoring protocols, as this represents a potential mechanism by over time, which intertidal deployments can positively impact adjacent subtidal communities.

In response to warming temperatures, species worldwide are expanding their range poleward. As these species move into new ecosystems, they interact with novel organisms and may alter food webs. Climate-driven expansions can thus lead to cascading changes in ecological interaction webs, affecting ecosystem function and services. However, climate change rarely acts in isolation. Other anthropogenic stressors, such as pollution and habitat conversion, act in concurrence with the indirect, biotic effects of climate change. In this study, we employed gut content analysis to investigate dietary similarity between sheepshead (Archosargus probatocephalus) and an expanding tropical congener, sea bream (A. rhomboidalis), in the Indian River Lagoon, Florida (USA). We paired these data with long-term seagrass and macroalgae monitoring data to investigate the effects of a macrophyte cover gradient, as a proxy for eutrophication-driven die-offs, on the diets of these fish species. Sheepshead and sea bream had highly similar diets, dominated by macrophytes. Furthermore, tropical sea bream consumed more seagrass at locations with higher seagrass abundance compared to low seagrass abundance, whereas sheepshead consumed a similar amount across seagrass abundance levels. These results suggest sea bream have the potential to negatively affect future recoveries of currently declining sheepshead populations. Invasion of sea bream may also hinder recovery of imperiled seagrass populations by increasing grazing pressure in this eutrophic, light-limited system. We provide evidence that the indirect effects of climate change and other anthropogenic stressors can interact and influence ecological interactions.

Drift macroalgae plays key roles in the ecology of many coastal systems, including the Indian River Lagoon. In the lagoon, changes in the biomass of drift macroalgae may have interacted with an unprecedented bloom of phytoplankton in 2011. Patterns in the biomass of drift macroalgae were identified using new and original analyses of data from several sampling programs collected between 1997 and 2019. All available data show a relatively low biomass of drift macroalgae in 2010–2012, and surveys of fixed transects and seining as part of a fisheries independent monitoring program also recorded low biomass in 2016. Low light availability and potentially stressful temperatures appeared to be the main influences as indicated by the results of incubations in tanks to determine environmental tolerances and data on ambient conditions. Decreased biomass of drift macroalgae had implications for cycling of nutrients because carbon, nitrogen, and phosphorus not stored in the tissues of drift macroalgae became available for uptake by other primary producers, including phytoplankton. The estimated 14–18% increases in concentrations of these elements in the IRL could have promoted longer and more intense phytoplankton blooms, which would have reduced light availability and increased stress on algae and seagrasses. An improved understanding of such feedback and the ecological roles played by drift macroalgae will support more effective management of nutrient loads and the system by accounting for cycling of nutrients among primary producers.

Seagrass is a major structural habitat in the Indian River Lagoon. Maps documented locations and areal extents of beds periodically since the 1940s, and surveys of fixed transects yielded changes in percent cover and depths at the end of the canopy since 1994. Areal extent increased by ∼7,000 ha from 1994 to 2009, mean percent cover within beds decreased from ∼40 to 20%, and mean percent cover standardized to maximum transect length remained near 20%. Thus, conditions supported a consistent biomass because cover decreased as areal extent increased. Between 2011 and 2019, ∼19,000 ha or ∼58% of seagrasses were lost, with offshore ends of canopies moving shoreward and shallower, and standardized mean percent cover decreased to ∼4%. These changes coincided with blooms of phytoplankton, and ≤ 27% of incident subsurface irradiance at 0.9 m was stressful. Decreases in mean percent cover per month of stress became larger when initial mean cover per transect was < 20%, which suggested that the ratio of aboveground to belowground tissues in the expanded and sparser beds led to respiratory demand that was not met by photosynthesis. Despite intermittent improvements in light penetration, widespread recovery of seagrasses has not occurred potentially due to detrimental feedbacks. For example, loss of seagrass exposed sediments to waves, and the resulting disturbance may have hampered recruitment of new shoots. The same decreases also made 58–88% of the carbon, nitrogen, and phosphorus in seagrass tissue available to other primary producers. These nutrients did not enhance growth of epiphytes, whose biomass decreased by ∼42%, but they apparently fueled blooms of phytoplankton, with mean chlorophyll-a concentrations increasing by > 900%. Such intense blooms increased shading and loss of seagrasses. Fortunately, data showed that patches of seagrasses at depths of 0.5–0.9 m persisted for 22–24 years, which suggested that this depth zone could hold the key to recovery. Nevertheless, optimistic estimates predict recovery could take 12–17 years. Such a long-term, widespread loss of a key structural habitat may generate multiple adverse effects in the system, and mitigating such effects may entail planting seagrasses to accelerate recovery.

Seven species of seagrass have been found in the Indian River Lagoon (IRL), making it an unusually diverse location at the global scale. From 1994 to 2019, the lagoon-wide distribution of these species reflected variations in temperature, salinity, and the availability of light at depth, which were related to latitudinal differences in hydrology and hydrodynamics along the IRL. In general, species richness was higher near the four southern inlets, and fewer species were found in areas with longer residence times for water. At a finer scale, the distribution of species varied among depths, with the greatest number of species found at mid-depths (~0.4–0.9 m). Prior to 2011, these patterns remained relatively consistent for ~ 40 years, but several, intense and prolonged phytoplankton blooms disrupted them. The areal extent of all seagrasses decreased by over 50%, the offshore ends of canopies moved shoreward and shallower, distributions of species along gradients of latitude and depth were disrupted, and mean percent cover decreased. Major changes in distribution and abundance of seagrasses arose when salinity, temperature, and availability of light at depth exceeded limits derived for each species. These substantial and widespread changes engendered concerns for recovery or rehabilitation of seagrasses in the lagoon.

There is a reciprocal relationship between disturbance and diversity such that disturbance can result in changes to diversity that in turn impact a population’s response to disturbance. Seagrasses are systems that are experiencing increasing disturbances and loss, and it is therefore important to understand this diversity–disturbance relationship. In this study, we observe changes in diversity and population differentiation of shoal grass ( Halodule wrightii ) during a large scale dieoff in the Indian River Lagoon, Florida USA. While allelic richness and heterozygosity were relatively high and do not change over time, population differentiation increased (estimated using F ST ), indicating genetic drift. This maintenance of diversity is important since seagrasses with high genetic diversity have been shown to withstand several environmental disturbances (grazing, low light, and high temperature) better than meadows with low diversity. This small increase in differentiation was only detectable because of replicate collection over time, which is rare in the literature and in monitoring programs. However, it is important since it indicates potential dispersal limitation which could hinder large scale recovery. We recommend plant nurseries as management tools in this system to preserve existing diversity and to aid in large scale restoration.

Locating significant deposits of muck (semifluid, fine-grained, organic-rich sediment) is an important aspect of estuarine conservation and management. The 38-kHz signal of a dual-frequency, single-beam acoustic survey of drift macroalgae was repurposed post hoc to locate and measure the horizontal and vertical extent of muck deposits within Indian River Lagoon, Florida. Raw echo returns were segmented into 5-cm strata of echo intensity, and a series of postprocessing algorithms were written to identify the characteristic pattern of backscatter associated with muck. Twenty-three deposits thicker than 0.5 m were identified within the 283-km(2) survey area, nearly all of which were found within depressions of the Indian River (IR). The quantity of muck was estimated at 1.87 x 10(6) m(3), roughly four times the quantity slated for removal from the Eau Gallie River and Elbow Creek (Florida) in 2016. The quantity of muck within the 110-km traverse of the IR Intracoastal Waterway (ICW) was estimated at 5.31 x 10(6) m(3). Muck deposits within the 31km traverse of the Banana River (BR) ICW were deeper (mu = 1.22 vs. 0.51 m), but muck volume was difficult to estimate because of the uncertain boundaries (i.e. channel width) of the BR ICW. The decision of whether to remove the significant volumes of muck within the 23 deposits and the ICW must consider ecological impact relative to concentrations of muck at the discharge of tributaries. Nonetheless, this extra layer of information was achieved with only a modest increase of surveying and postprocessing effort. Synergies such as this will be important in an era of monitoring and management cost constraints.

During the spring of 2011 an unprecedented “Super” algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European Space Agency’s MEdium Resolution Imaging Spectrometer (MERIS) platform provided multispectral data at 665 and 708 nm, which was used to quantify the phytoplankton Chl a by fluorescence while minimizing the effects of other water column constituents. The three objectives were to: (1) calibrate and validate two Chl a algorithms using all available MERIS data of the IRL from 2002 to 2012; (2) determine the accuracy of the algorithms estimation of Chl a before, during, and after the 2011 super bloom; and (3) map the 2011 algal bloom using the Chl a algorithm that was proven to be effective in other similar estuaries. The chosen algorithm, Normalized Difference Chlorophyll Index (NDCI), was positively correlated with the in-situ measurements, with an R2 value of 0.798. While there was a significant (62.9 ± 25%) underestimation of Chl a using MERIS NDCI, the underestimation appears to be consistent across the data and mostly in the estimations of lower concentrations, suggesting that a qualitative or ratio analysis is still valid. Analysis of the application of the NDCI processed MERIS data provided additional insights that the in-situ measurements were unable to record. The time series MERIS Chl a maps along with in-situ water quality monitoring data depicted that the 2011 IRL bloom started after a heavy rainfall in March 2011 and peaked in October 2011 after a decrease in temperature. The bloom collapse also coincided with heavy rainfall and rapidly decreasing temperatures and salinity through October to November 2011.

Relationships between shifts in climatic and other environmental conditions and changes in the character and dynamics of phytoplankton blooms were examined in three interconnected subtropical lagoons on the east coast of Florida, i.e., Mosquito Lagoon, Indian River Lagoon, and Banana River Lagoon, from 1997 to 2013. Phytoplankton blooms were a common feature through most of the study period in two of the lagoons. From 1997 to 2009, blooms in the latter two lagoons were typically dominated by dinoflagellates in the warm wet season and diatoms in the cool dry season. Blooms of the dominant bloom-forming dinoflagellate species Pyrodinium bahamense were positively correlated to rainfall levels, indicating a link to enhanced external nutrient loads. In 2011–2013, major blooms were observed in all three lagoons, but unlike the previous 14 years, they were dominated by picoplanktonic eukaryotes, including a chlorophyte, Pedinophyceae sp., and the brown tide species Aureoumbra lagunensis. The results suggest that extreme climatic conditions, including record cold winter water temperatures and low rainfall levels, were major driving factors in this state shift in the character of blooms, through a wide range of effects including die-offs of benthic flora and fauna, suppression of grazer populations, alteration of nutrient regimes, and uncharacteristic water column conditions, such as elevated salinities and light attenuation.

Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, Slope(RED) and Slope(NIR). Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63-64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.

The Indian River Lagoon (IRL), FL, USA, is arguably the most biologically diverse estuarine system in the continental USA.An important part of any marine ecosystem, seagrass beds provide food, habitat, and nursery for both vertebrate and invertebrate inhabitants.Their high productivity and sensitivity to water quality changes make seagrasses primary indicators of overall coastal ecosystem health.Halophila johnsonii (Johnson's seagrass), currently federally listed as a threatened species, is one of the rarest seagrass species, and is found within the IRL usually only approximately between Sebastian and Jupiter Inlets, continuing south to northern Biscayne Bay.Quantitative data for the present study is a subset of the monitoring of all 7 seagrass species in the IRL starting in 1994 with 1 m 2 quadrats placed every 10 m along each transect from shore to the deep edge of the seagrass bed.The present study reports long-term data for H. johnsonii from the 35 transects within its range in the IRL sampled continuously (summer and winter) from 1994 through 2007.Our objective was to provide insight and to test hypotheses about the dynamic nature of H. johnsonii.In addition, we developed an index of retrospective effect size to assess the importance of ecological factors and their interactions as well as to develop a comparative basis for future seagrass studies.This erratic, sparse, but persistent species increased slightly in coverage over time but with accompanying high variability so that regional stability over time is maintained by local unpredictability.Summer abundance on average appears to follow a 2 to 3 yr increase, then a single year's precipitous decrease over the 14 yr of our summer observations.Based upon the results from this long-term and thorough data set, we propose a new model of asynchronous, 'pulsating patches' in both space and time for describing the long-term survival strategy of H. johnsonii.

Seagrasses have been considered one of the most critical marine habitat types of coastal and estuarine ecosystems such as the Indian River Lagoon. They are an important part of biological productivity, nutrient cycling, habitat stabilization and species diversity and are the primary focus of restoration efforts in the Indian River Lagoon. The areal extent of seagrasses has declined within segments of the lagoon over the years. Light availability to seagrasses is a major criterion limiting their distribution. Decreased water clarity and resulting reduced light penetration have been cited as the major factors responsible for the decline in seagrasses in the lagoon. Hence, light is a critical factor for the survival of seagrass species. Light attenuation coefficient is an important parameter that indicates the light attenuated by the water column and can therefore be used as an indicator of seagrass vigor. A number of region-specific linear light attenuation models have been proposed in the literature. Though, in practice, linear light attenuation models have been commonly used, there is need for a flexible and robust model that incorporates the non-linearities present in coastal and estuarine environments. This paper presents a neural network based model to estimate light attenuation coefficient from water quality parameters and thereby indirectly monitor seagrass population in the Indian River Lagoon. The proposed neural network models were compared with linear regression models, step-wise linear regression models, model trees and support vector machines. The neural network models performed fairly better compared to the other models considered.

Between August 14 and September 26, 2004, four tropical weather systems (Charley, Frances, Ivan, and Jeanne) affected the central Indian River Lagoon (IRL). The central IRL received a prodigious amount of rainfall for the 2 mo, between 72 and 83 cm, which is a once-in-50-yr rainfall event. High stream discharges were generated that, combined with wind-suspended sediments, significantly reduced salinities and water transparency. In September, salinities among central IRL segments dropped from 30 psu or more to ≤15 psu, color increased from a low of 10 pcu to ≥100 pcu, and turbidity increased from ≤3 NTU up to 14 NTU. Evidence of the hurricanes' physical effects on seagrasses (burial, no scour) was limited to just one of the more than 25 sites inspected. Within 2 to 3 mo following the hurricane period, most parameters related to water transparency returned to or showed improvement over their prehurricane (February–July 2004) levels. Unseasonably low salinities (<20 psu) and moderately high color (>20 pcu) were observed through spring 2005, largely attributable to a relatively long residence time and a wetter-than-average spring season in 2005. By the end of the study period (July 2006), the central IRL generally showed a continuation of two opposite seagrass trends—an increase in depthlimit coverage but a decline in coverage density—that began before 2004. Also, within a limited reach of the central IRL, there was a temporary shift in species composition in summer 2005 ( Ruppia maritima increased as Halodule wrightü decreased). It is likely that the persistently low salinities (not color) in 2004–2005 affected the species composition and coverage density. This study reveals that seagrasses are resilient to the acute effects of hurricanes and underscores the need to reduce chronic, an thropogenic effects on seagrasses.

Seagrass protection and restoration in Florida’s Indian River Lagoon system (IRLS) is a mutual goal of state and federal programs. These programs require, the establishment of management targets indicative of seagrass recovery and health. We used three metrics related to seagrass distribution: areal coverage, depth limit, and light requirement. In order to account for the IRLS’s spatial heterogeneity and temporal variability, we developed coverage and depth limit targets for each of its 19 segments. Our method consisted of two steps: mapping the union of seagrass coverages from all availabe mapping years (1943, 1986, 1989, 1992, 1994, 1996, and 1999) to delineate wherever seagrass had been mapped and determining the distribution of depth limits based on 5,615 depth measurements collected on or very near the deep-edge boundary of the union coverage. The frequency distribution of depth limits derived from the union coverage, along with the median (50th percentile) and maximum (95th percentile) depth limits, serve as the seagrass depth targets for each segment. The median and maximum depth targets for the IRLS vary among segments from 0.8 to 1.8 and 1.2 to 2.8 m, respectively.Halodule wrightii is typically the dominant seagrass species at the deep-edge of IRLS grass beds. We set light requirement targets by using a 10-yr record of light data (1990–1999) and the union coverage depth limit distributions from the most temporally stable seagrass segments. The average annual light requirement, based on the medians of the depth limit distributions, is 33 ± 17% of the subsurface light. The minimum annual light requirement, based on of the 95th percentile of the depth distributions, is 20 ± 14%; the minimum growing season light requirement (March to mid September) is essentially the same (20 ± 13%). Variation in depth limits and light requirements, is probably due to factors other than light that influence the depth limit of seagrasses (e.g., competition, physical disturbance). The methods used in this study are robust when applied to large or long-term data sets and can be applied to other estuaries where grass beds are routinely monitored and mapped.

Three areas of the Indian River Lagoon, Florida (USA) were surveyed to show seasonal changes in the distribution and biomass of macroalgae and seagrass. Acoustic seafloor discrimination based on first and second echo returns of a 50 kHz and 200 kHz signal, and two different survey systems (QTCView and ECHOplus) were used. System verification in both the field and a controlled environment showed it was possible to distinguish acoustically between seagrass, sparse algae, and dense algae. Accuracy of distinction of three classes (algae, seagrass, bare substratum) was around 60%. Maps were produced by regridding the survey area to a regular grid and using a nearest-neighbor interpolation to provide filled polygons. Biomass was calculated by counting pixels assigned to substratum classes with known wet-weight biomass values (sparse algae 250 g m−2, dense algae 2000 g m−2, seagrass 100 g m−2) that were measured in the field. In three study areas (Melbourne, Sebastian Inlet, and Cocoa Beach), a dependence of algal biomass on depth and season was observed. Seagrass most frequently occurred in water less than 1 m deep, and in November, seagrass beds tended to be covered by dense algae that also extended up- and downstream of shoals in the Lagoon. In March, the pattern was similar, with the exception that some areas of previously dense algae had started thinning into sparse algae. Macrophyte biomass was lowest in May in the Melbourne and Cocoa Beach study areas, with the opposite situation in the Sebastian Inlet study area. In May, seagrass areas were largely devoid of dense algae and most algae accumulations were sparse. In August, dense algae covered large areas of the deep Lagoon floor while shoals were largely free of algae or had only sparse cover. We suggest this summer pattern to reflect moribund algae being washed from the shallows to deeper channels and from there being removed from the lagoonal ecosystem either through tidal passages into the open ocean or by degradation and breakdown in situ. The differences between the study areas indicate high spatial and temporal variability in biomass and distribution of macrophyte biomass in the Indian River Lagoon.

Light availability to seagrasses is a major criterion limiting the distribution of seagrasses. Decreased water clarity and resulting reduced light penetration have been cited as major factors responsible for the decline in seagrasses. Light attenuation coefficient is an important parameter that indicates the light attenuated by the water column and can thereby be an indicator of seagrass health. Though, in practice, linear light attenuation models have been commonly used, there is a need for a more accurate model that can take into account the non-linearities present in coastal and estuarine environments. This paper presents neural network-based light attenuation models for monitoring the seagrass health in the Indian River Lagoon, FL. For performance evaluation, results of the developed neural network models are compared with linear regression models, model trees, and support vector machines.

Seagrass both disappeared and recovered within 4 yr in one region of northern Indian River Lagoon (IRL). For the specific area referred to as Turnbull Bay, a relatively pristine area of the IRL, over 100 ha of seagrass completely disappeared from 1996 to 1997 and then recovered by 2000. Based on lagoon-wide mapping from aerial photographs taken every 2–3 years since 1986, coverage of seagrass in Turnbull Bay declined from 124 ha in 1989 to 34 ha by 1999 and increased to 58 ha in 2003. Bi-annual monitoring of fixed seagrass transects tells a more detailed story. Species composition along the Turnbull transect shifted fromHalodule wrightii toRuppia maritima beginning in 1995, and macroalgal abundance increased. By the summer of 1997, seagrass completely disappeared along the transect, as well as in most of the surrounding areas in Turnbull Bay; macroalgae covered much of the sediment surface. No significant water quality changes were detected. Light attenuation and suspended solid values did increase after the seagrass disappeared. Porewater sulfide concentrations, taken after all the grass was gone in 1997, were high (2,000 μM), but did improve by 1998 (1,200 μM). Seagrass recovery was rapid and occurred in the reverse sequence of species loss. Seedlings ofR. maritima were the first colonizers, then patches ofH. wrightii appeared. In 2000,Halophila engelmannii returned in the deeper water (>0.6m). By the summer of 2000, the beds had completely recovered. We conclude that this demise was a natural event caused by a long-term buildup of seagrass biomass and a thick (10–15 cm) layer of organic detritus and ooze. We surmise that such a crash and subsequent recovery may be a natural cycle of decline and recovery within this semirestricted, poorly-flushed area. The frequency of this cycle remains uncertain.

Seagrasses are marine plants that provide many services such as primary productivity, food web interactions, shelter, nutrient cycling and habitat stabilization that are essential to marine and estuarine ecosystems. Therefore, monitoring seagrass health is crucial for the existence of many marine aquatic plants and animals. The minimal light requirement of seagrasses is about 20-30% of the total light measured just below the surface. This is relatively high compared to terrestrial plants and phytoplankton, which underlines the importance of water transparency for these species. Hence, light penetration into estuarine waters is critical for seagrass survival. In this paper, an approach to estimate the light attenuation coefficient from water quality parameters using neural networks is proposed. The model is compared with linear regression models such as step-wise linear regression and linear least squares regression. The light attenuation model presented here can be used for monitoring water quality and thereby seagrass health.